A computer network, such as the Internet or World Wide Web, typically serves to connect users to the information, content, or other resources that they seek. Web content, for example, varies widely both in type and subject matter. Examples of different content types include, without limitation: text documents; audio, visual, and/or multimedia data files. A particular content provider, which makes available a predetermined body of content to a plurality of users, must steer a member of its particular user population to relevant content within its body of content.
For example, in an automated customer relationship management (CRM) system, the user is typically a customer of a product or service who has a specific question about a problem or other aspect of that product or service. Based on a query or other request from the user, the CRM system must find the appropriate technical instructions or other documentation to solve the user's problem. Using an automated CRM system to help customers is typically less expensive to a business enterprise than training and providing human applications engineers and other customer service personnel. According to one estimate, human customer service interactions presently cost between $15 and $60 per customer telephone call or e-mail inquiry. Automated Web-based interactions typically cost less than one tenth as much, even when accounting for the required up-front technology investment.
One ubiquitous navigation technique used by content providers is the Web search engine. A Web search engine typically searches for user-specified text, either within a document, or within separate metadata associated with the content. Language, however, is ambiguous. The same word in a user query can take on very different meanings in different context. Moreover, different words can be used to describe the same concept. These ambiguities inherently limit the ability of a search engine to discriminate against unwanted content. This increases the time that the user must spend in reviewing and filtering through the unwanted content returned by the search engine to reach any relevant content. As anyone who has used a search engine can relate, such manual user intervention can be very frustrating. User frustration can render the body of returned content useless even when it includes the sought-after content. When the user's inquiry is abandoned because excess irrelevant information is returned, or because insufficient relevant information is available, the content provider has failed to meet the particular user's needs. As a result, the user must resort to other techniques to get the desired content. For example, in a CRM application, the user may be forced to place a telephone call to an applications engineer or other customer service personnel. As discussed above, however, this is a more costly way to meet customer needs. For these and other reasons, the present inventors have recognized the existence of an unmet need to provide improved tools and techniques for searching for text in content data or metadata using textual and/or other input(s) obtained from a user.